{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\target_all_period.csv\")\n",
    "df['number_of_publications']/=16\n",
    "df.sort_values('number_of_publications', ascending=False, inplace=True)\n",
    "df['status']=df['number_of_publications'].apply(lambda x: 'HP'  if x>=72 else 'N')\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>dist</th>\n",
       "      <th>share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HP</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N</td>\n",
       "      <td>135209</td>\n",
       "      <td>0.999867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  status    dist     share\n",
       "0     HP      18  0.000133\n",
       "1      N  135209  0.999867"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df=df.groupby('status')['dist'].sum().reset_index()\n",
    "result_df['share']=result_df['dist']/result_df['dist'].sum()\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "135227"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dist'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "latest_affiliation_country\n",
       "fra    3\n",
       "can    3\n",
       "deu    2\n",
       "gbr    2\n",
       "swe    1\n",
       "tha    1\n",
       "usa    1\n",
       "fin    1\n",
       "hkg    1\n",
       "sau    1\n",
       "jpn    1\n",
       "chn    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['status']=='HP'].latest_affiliation_country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\total_all_period.csv\")\n",
    "df2['number_of_publications']/=16\n",
    "df2.sort_values('number_of_publications', ascending=False, inplace=True)\n",
    "df2['status']=df2['number_of_publications'].apply(lambda x: 'HP'  if x>=72 else 'N')\n",
    "df2.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>dist</th>\n",
       "      <th>share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HP</td>\n",
       "      <td>69</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N</td>\n",
       "      <td>14084609</td>\n",
       "      <td>0.999995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  status      dist     share\n",
       "0     HP        69  0.000005\n",
       "1      N  14084609  0.999995"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df2=df2.groupby('status')['dist'].sum().reset_index()\n",
    "result_df2['share']=result_df2['dist']/result_df2['dist'].sum()\n",
    "result_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14084678"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['dist'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['status']=='HP'].latest_affiliation_country.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expanded_values = np.repeat(df['number_of_publications'].values, df['dist'].values)\n",
    "expanded_values2 = np.repeat(df2['number_of_publications'].values, df2['dist'].values)\n",
    "\n",
    "# Create the histogram using seaborn\n",
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "\n",
    "# Plot histograms with labels\n",
    "sns.histplot(expanded_values2, kde=False, edgecolor='black', alpha=0.7, discrete=True, label='Total')\n",
    "sns.histplot(expanded_values, kde=False, edgecolor='black', alpha=0.7, discrete=True, label='Target mention')\n",
    "\n",
    "\n",
    "# Labels and title\n",
    "plt.xlabel('Average yearly publication')\n",
    "plt.ylabel('Count')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.axvspan(72,165, linestyle=':', color='k', alpha=0.3)\n",
    "plt.xlim(0, 165)\n",
    "# Add legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2608695652173913"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "18/69"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00960100046305638"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dist'].sum()/df2['dist'].sum()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
